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#' Get the size of a single panel
#'
#' @param n the number of panels
#' @param len the overall length of the side
#' @param margin the outer margin width (will be multiplied by 2)
#' @param gutter the space between panels
#'
#' @return a numeric value specifying the panel width/length
#'
#' @examples

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ade4phi <- function(sig){
  n <- length(sig)
  f <- rep(0, n - 1)
  if (length(sig) == 3) {
    f <- rep(0, 1)
  }
  f[1] <- (sig[n] - sig[n - 1]) / sig[n]
  if (length(f) > 1) {
    s1 <- cumsum(sig[(n - 1):2])[-1]

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#!/usr/bin/env python3
#
# This script will BLAST a series of GenBank IDs representing SSR sequences from
# Sirjusingh, C., Kohn, L.M., 2001. Characterization of microsatellites in the 
# fungal plant pathogen, Sclerotinia sclerotiorum. Molecular Ecology 1, 267e269.
#
# This uses biopython to download the XML, save it to a file in the working 
# directory, and then looks for matches that have Sclerotinia chromosome in the
# title and has a score of > 100. If there's a better way to do this, I would
# like to know it. Currently, it can download a few results at a time and then

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# shruggie
# Source: https://twitter.com/climagic/status/672862397015658496
function shrug(){ echo -n "¯\_(ツ)_/¯" | pbcopy;echo "¯\_(ツ)_/¯ copied to your clipboard"; }
export -f shrug

# strollie
function stroll(){ echo -n "ᕕ( ᐛ )ᕗ" | pbcopy; echo "ᕕ( ᐛ )ᕗ copied to your clipboard"; }
export -f stroll

# flippy

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# This is from @drob on twitter:
#
# https://twitter.com/drob/status/915378838078722048
#
library("purrr")
transition_mc <- function(steps, start, mat){
  i <- seq_len(nrow(mat))
  transition <- ~ sample(i, prob = (i == .) %*% mat)
  accumulate(seq_len(steps), transition, .init = start)
}

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#' paste function that replaces NA characters with blanks
#'
#' All arguments are passed to paste.
#'
#' Usage: 
#' pastena(letters, sample(c(1:10, NA), 26, replace = TRUE))
pastena <- function(..., collapse = NULL, sep = " ") {
  dots <- list(...)
  dots <- lapply(dots, function(i) ifelse(is.na(i), "", i))
  res  <- do.call("paste", c(dots, list(collapse = collapse, sep = sep)))

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#!/usr/bin/env bash
# This ridiculous script will take in a file as input and spit out comma-separated text for easier parsing.
# Usage: 
# $ gvcf2csv.sh GVCF.err | column -t -s,
grep ProgressMeter $1 | \
awk -F" - " '{print $2}' | \
tail -n +2 | \
sed -E 's/[ ]\|[ ]/,/g' | \
sed -E "s/([0-9hmsw%])[ ]{3,}([0-9])/\1,\2/g"

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FILES := file1.out \
  file2.out \
  file3.out \
  file4.out \
  file5.out \
  file6.out \
  file7.out \
  file8.out \
  file9.out

zkamvar

# This will take a list of dois and convert them
# to a table containing the names of coauthers to
# populate a conflict of interest list.
# 
# It's not perfect, but it's better than nothing.
#
library("rcrossref")
library("tidyverse")
#
# Change these to YOUR dois.

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# Note: this assumes that you have a custom library at the top of your .libPaths()
fields <- c("Package", "LibPath", "Version", "Priority", "Depends", "Imports", "LinkingTo", "Suggests", "Enhances", "OS_type", "Built", "RemoteSha", "Packaged", "biocViews")
x <- installed.packages(.libPaths()[1], fields = fields)
needs_update <- x[x[, "Built"] != "3.4.0", ]
bicond <- !is.na(needs_update[, "biocViews"])
ghub   <- !is.na(needs_update[, "RemoteSha"])
remote <- !is.na(needs_update[, "Packaged"])
bioconductor <- needs_update[bicond, ]
cran         <- needs_update[!bicond & !ghub & remote, ]
install.packages(rownames(cran))